It is often desirable to ensure that a printed document has not been altered or tampered with in some unauthorised manner from the time the document was first printed. For example, a contract that has been agreed upon and signed on a particular date may subsequently be fraudulently altered and it is desirable to be able to detect such alterations in detail. Similarly, security documents of various sorts, such as cheques and other monetary instruments record values which are vulnerable to fraudulent alteration. Detection of any fraudulent alteration in such documents is also desirable. Further, it is desirable that such detection be automated, and that the detection reveals the nature of any alteration.
Various methods of document tamper detection have been proposed and used.
One approach to tamper detection uses watermarks or two-dimensional (2D) barcodes printed on the document to encode information, or a representation, about the contents of the original document. When the document contents are to be verified, the information is extracted from the watermark or 2D barcode and compared to the candidate document. Any changes between the encoded representation of the original document and the candidate document represent possible instances of tamper.
The process of tamper detection is complicated by the fact that a candidate document may have been subject to noise. For example, printing, scanning and handling a document alters its appearance through the introduction of unintended noise. It is desirable for tamper detection system to be robust to noise. In other words, document noise should not be identified as tamper.
Tamper detection systems typically use a noise threshold in order to distinguish noise from tamper. If the difference between the original document and the candidate document in a region is less than the noise threshold, the region is assumed to be free of tamper. However, if the difference between the original document and the candidate document in a region is greater than the noise threshold, the region is presumed to contain tamper.
Various methods for setting noise thresholds have been proposed. A common method is to empirically determine a suitable noise threshold based on experimental data. The noise threshold is then hard coded into the tamper detection system. A disadvantage of this approach is that the threshold has to be set high enough to avoid false tamper detections under a wide range of conditions, thereby necessitating a threshold that misses small instances of true tamper. Furthermore, documents with an unusually high degree of noise are likely to result in a large number of false tamper detections. Such an approach may satisfactory handle printer and scanner noise, which can usually be reliably modelled, but generally fails to accommodate handling noise and multiple copy generations.
Another common method for selecting a suitable noise threshold is to allow the user to manually adjust the threshold. The user is able to visually estimate the level of noise in a document, and set the noise threshold to a value that minimises the number of false tamper detections. Disadvantages of this approach are that it is subjective and open to abuse. Furthermore, since it requires user input it is not suitable for fully automated workflows.
It is an object of the present invention to substantially overcome, or at least ameliorate, one or more disadvantages of existing arrangements.